Web analytics is the measurement, collection, analysis, and reporting of web data. Web analytics was originally developed to monitor web data by analyzing log files or by inserting small scripts or page tags that track activity. For example, in some web analytics techniques, a small Javascript code or “cookie” may be associated with a website to collect variables such as time spent on a site or the identity of an originating website. Web analytics techniques, however, are no longer merely used to monitor web data. Nowadays, web analytics are also actively used to support web development projects.
Web development and software engineering is an increasingly complex activity. Current projects are large and intricate, may require multiple versions or iterations (often being developed in parallel), and may be developed by multiple teams that independently work on specific features. It is not unusual for major web development efforts to experience delays or failures caused by, for example, unexpected interactions between website features. To face these complexities and guarantee accurate control of the project, developers have turned to web analytics to help monitor the status of a project, compare different iterations, and evaluate the effect of new features in the overall user experience. For example, during web development page tags may be used to monitor whether buttons and links are working properly. Also, a page tag may track whether a login is successful when a user inputs accurate credentials and the loading time of a website. This incorporation of web analytics or metadata analysis in web development has helped developers and managers to monitor projects.
Nowadays, however, projects scale very quickly and the vast amount of page tags or metadata associated with a website often prevents their effective analysis or use during development. For example, it is common to have hundreds or even thousands of page tags associated with a website. Developers and managers must now invest massive resources to monitor these page tags during development. Indeed, analyzing page tags associated with a large website may stress computer resources because parsing and filtering tasks are performed in very large data sets, making the analysis processes slow and inefficient. Further, monitoring page tags often require multiple iterations of analysis for each sprint/regression cycle. In some situations the analysis of the page tags may create bottle necks in the development process significantly delaying a project. The increasing large amount and complexity of page tags that are associated with a website, can render the use of web analytics techniques impractical during development, and post-development evaluation.
The disclosed systems and methods address one or more of the problems set forth above and/or other problems in the prior art.